dblogr/

R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo
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Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3 
## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) 
## [1] 8
# 2 * 2 * 2 = 8
2^3 
## [1] 8
# 8 * 8 = 64
sqrt(64) 
## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) 
## [1] 8

Functions

has many useful built in functions

1:10
##  [1]  1  2  3  4  5  6  7  8  9 10
as.character(1:10)
##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"
rep(1:2, times = 5)
##  [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)
##  [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)
##  [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)
## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)
##  [1]  5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)
## [1]  5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")
##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"
paste(1:10, collapse = "-")
## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)
##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"
min(1:10)
## [1] 1
max(1:10)
## [1] 10
range(1:10)
## [1]  1 10
mean(1:10)
## [1] 5.5
sd(1:10)
## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)
## [1] "10 %"
customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)
## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

for loops and if else statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx
##  [1]  3  6  9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2
##  [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}
## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")
##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))
##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd"  "24 is Even" "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

  • c(): a string of values
  • matrix(): a two dimensional matrix in one format
  • data.frame(): a two dimensional matrix where each column can be a different format
  • list():

A string…

xc <- 1:10
xc
##  [1]  1  2  3  4  5  6  7  8  9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc
##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd
##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]
##  [1]  1  2  3  4  5  6  7  8  9 10
xl[[2]]
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100
xl[[3]]
##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc
## [1] 5
xd$x3[5] # 5th element in col "x3"
## [1] 1
xd[5,"x3"] # row 5, col "x3"
## [1] 1
xd$x3 # all of col "x3"
##  [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"
##  [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols
##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"
##   x4 x5
## 2  2  2
## 4  2  4
xl[[3]]$x1 # 3rd object in the list, col "x1
##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx
##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

  • numeric
  • integer
  • character
  • factor
  • logical
xd$x3 <- as.character(xd$x3)
xd$x3
##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10
##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0
##  [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)
## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()
##  num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()
##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()
## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...
str(xl) # list()
## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")
library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx
##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))
##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx
##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000
xx$Data1 < 5 # which are less than 5
##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]
##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]
##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx
##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000
filter(xx, Data1 < 5)
##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000
xx %>% filter(Data1 < 5)
##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000
xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)
##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110
xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs
## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.
xx %>% left_join(xs, by = "Group")
##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy
## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy
## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy
## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8
yy <- yy %>% spread(key = Name, value = Value)
yy
## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru
##   [1] 76.0514160 97.0007216 36.7862846 63.4855455 45.0386635 86.3331151 42.3964209 92.2434885 85.3544575 63.6604202 40.1192845 83.0220876
##  [13] 79.5576285  9.3404625 71.9361121 96.6541874  6.5748170  7.6666445 85.6546566 38.1267829 80.1846915 44.6780257 43.6088299 59.9759942
##  [25] 35.2009352 74.5780286  8.7182840 54.1685727  8.9574384 27.0577118 82.7553758  6.1199085 83.9581907 12.6711393 36.0259232 64.9716008
##  [37] 83.8744597 14.7391804 19.3391845 30.6301468 26.2344800 55.3421607 84.6075232 66.0928408 81.1340251 31.1349039 29.7696615 40.5406700
##  [49] 89.1319551 47.2515983 53.1120910 91.4317689 78.3857649 37.2080421 95.5670227 15.4168682 71.4056405 19.5588506  1.0247018 98.3961527
##  [61] 27.1747704 62.0444560 93.9069817 23.8572619 19.3939156  6.0260451 73.2076574 86.1800733 94.4535479 74.1504201 69.5930754 72.9850982
##  [73] 74.6167744 84.1828492 61.2428303 62.3945256 61.5748227 63.0234935 32.4409686 68.2879426 46.9218516 40.6345759 42.6559645 55.4282784
##  [85] 72.0958567  4.7745049 59.7881568  5.5195824 99.7146011  0.3229898  7.4357566 31.5081340 16.9763986 23.9394932 69.6627819 87.8663652
##  [97] 72.9332758 90.0638067 74.8563739 50.4444058
plot(x = ru)

order(ru)
##   [1]  90  59  86  88  66  32  17  91  18  27  29  14  34  38  56  93  39  65  58  64  94  41  30  61  47  40  46  92  79  25  35   3  54  20
##  [35]  11  48  82   7  83  23  22   5  81  50 100  51  28  42  84  87  24  75  77  62  76  78   4  10  36  44  80  71  95  57  15  85  97  72
##  [69]  67  70  26  73  99   1  53  13  21  45  31  12  37  33  74  43   9  19  68   6  96  49  98  52   8  63  69  55  16   2  60  89
ru<- ru[order(ru)]
ru
##   [1]  0.3229898  1.0247018  4.7745049  5.5195824  6.0260451  6.1199085  6.5748170  7.4357566  7.6666445  8.7182840  8.9574384  9.3404625
##  [13] 12.6711393 14.7391804 15.4168682 16.9763986 19.3391845 19.3939156 19.5588506 23.8572619 23.9394932 26.2344800 27.0577118 27.1747704
##  [25] 29.7696615 30.6301468 31.1349039 31.5081340 32.4409686 35.2009352 36.0259232 36.7862846 37.2080421 38.1267829 40.1192845 40.5406700
##  [37] 40.6345759 42.3964209 42.6559645 43.6088299 44.6780257 45.0386635 46.9218516 47.2515983 50.4444058 53.1120910 54.1685727 55.3421607
##  [49] 55.4282784 59.7881568 59.9759942 61.2428303 61.5748227 62.0444560 62.3945256 63.0234935 63.4855455 63.6604202 64.9716008 66.0928408
##  [61] 68.2879426 69.5930754 69.6627819 71.4056405 71.9361121 72.0958567 72.9332758 72.9850982 73.2076574 74.1504201 74.5780286 74.6167744
##  [73] 74.8563739 76.0514160 78.3857649 79.5576285 80.1846915 81.1340251 82.7553758 83.0220876 83.8744597 83.9581907 84.1828492 84.6075232
##  [85] 85.3544575 85.6546566 86.1800733 86.3331151 87.8663652 89.1319551 90.0638067 91.4317689 92.2434885 93.9069817 94.4535479 95.5670227
##  [97] 96.6541874 97.0007216 98.3961527 99.7146011
plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd
##   [1] 51.17472 61.46792 28.75985 62.19491 51.67518 58.05739 46.90093 60.77104 51.52682 51.89670 52.88551 58.54176 58.10199 48.12979 41.02931
##  [16] 32.22980 51.01030 64.08398 56.29558 53.29418 37.71354 60.71447 33.90922 45.32330 48.57791 58.95882 25.29998 51.30558 59.77403 41.87121
##  [31] 49.05083 59.94059 51.16073 54.93897 58.64274 60.47314 48.61174 28.89812 33.17733 70.07425 48.13471 58.42176 31.43321 58.03661 64.89916
##  [46] 40.89735 38.74091 67.59108 43.39537 53.07352 44.71255 33.74264 59.55659 52.01518 46.71520 53.66865 55.19843 32.91487 61.89269 61.29364
##  [61] 44.25803 44.54051 54.02185 49.22725 47.64417 52.57723 39.60429 37.83434 32.58520 54.85639 30.19256 59.12818 49.87350 48.45823 57.13926
##  [76] 39.12326 45.75490 52.15874 52.70051 58.55840 61.82856 44.92077 53.14844 49.80901 44.72684 38.81185 42.63935 39.04751 48.36225 47.06450
##  [91] 62.11352 47.51570 52.61164 53.81128 53.91561 47.05708 25.62752 36.50422 55.44554 42.02247
nd <- nd[order(nd)]
nd
##   [1] 25.29998 25.62752 28.75985 28.89812 30.19256 31.43321 32.22980 32.58520 32.91487 33.17733 33.74264 33.90922 36.50422 37.71354 37.83434
##  [16] 38.74091 38.81185 39.04751 39.12326 39.60429 40.89735 41.02931 41.87121 42.02247 42.63935 43.39537 44.25803 44.54051 44.71255 44.72684
##  [31] 44.92077 45.32330 45.75490 46.71520 46.90093 47.05708 47.06450 47.51570 47.64417 48.12979 48.13471 48.36225 48.45823 48.57791 48.61174
##  [46] 49.05083 49.22725 49.80901 49.87350 51.01030 51.16073 51.17472 51.30558 51.52682 51.67518 51.89670 52.01518 52.15874 52.57723 52.61164
##  [61] 52.70051 52.88551 53.07352 53.14844 53.29418 53.66865 53.81128 53.91561 54.02185 54.85639 54.93897 55.19843 55.44554 56.29558 57.13926
##  [76] 58.03661 58.05739 58.10199 58.42176 58.54176 58.55840 58.64274 58.95882 59.12818 59.55659 59.77403 59.94059 60.47314 60.71447 60.77104
##  [91] 61.29364 61.46792 61.82856 61.89269 62.11352 62.19491 64.08398 64.89916 67.59108 70.07425
plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)
## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))
##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32932 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)
## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)
## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

R Markdown

Tutorials on how to create an R markdown document like this one can be found here:


dblogr/


© Derek Michael Wright